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| TITLE = "Alpaca-LoRA Playground" | |
| ABSTRACT = """ | |
| Thanks to [tolen](https://github.com/tloen/alpaca-lora), this application runs Alpaca-LoRA which is instruction fine-tuned version of [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/). This demo currently runs 30B version on a 3*A6000 instance at [Jarvislabs.ai](https://jarvislabs.ai/). | |
| NOTE: too long input (context, instruction) will not be allowed. Please keep context < 500 and instruction < 150 | |
| """ | |
| BOTTOM_LINE = """ | |
| This demo application runs the open source project, [Alpaca-LoRA-Serve](https://github.com/deep-diver/Alpaca-LoRA-Serve). By default, it runs with streaming mode, but you can also run with dynamic batch generation model. Please visit the repo, find more information, and contribute if you can. | |
| Alpaca-LoRA is built on the same concept as Standford Alpaca project, but it lets us train and inference on a smaller GPUs such as RTX4090 for 7B version. Also, we could build very small size of checkpoints on top of base models thanks to [π€ transformers](https://huggingface.co/docs/transformers/index), [π€ peft](https://github.com/huggingface/peft), and [bitsandbytes](https://github.com/TimDettmers/bitsandbytes/tree/main) libraries. | |
| We are thankful to the [Jarvislabs.ai](https://jarvislabs.ai/) who generously provided free GPU instances. | |
| """ | |
| DEFAULT_EXAMPLES = { | |
| "Typical Questions": [ | |
| { | |
| "title": "List all Canadian provinces in alphabetical order.", | |
| "examples": [ | |
| ["1", "List all Canadian provinces in alphabetical order."], | |
| ["2", "Which ones are on the east side?"], | |
| ["3", "What foods are famous in each province on the east side?"], | |
| ["4", "What about sightseeing? or landmarks? list one per province"], | |
| ], | |
| }, | |
| { | |
| "title": "Tell me about Alpacas.", | |
| "examples": [ | |
| ["1", "Tell me about alpacas in two sentences"], | |
| ["2", "What other animals are living in the same area?"], | |
| ["3", "Are they the same species?"], | |
| ["4", "Write a Python program to return those species"], | |
| ], | |
| }, | |
| { | |
| "title": "Tell me about the king of France in 2019.", | |
| "examples": [ | |
| ["1", "Tell me about the king of France in 2019."], | |
| ["2", "What about before him?"], | |
| ] | |
| }, | |
| { | |
| "title": "Write a Python program that prints the first 10 Fibonacci numbers.", | |
| "examples": [ | |
| ["1", "Write a Python program that prints the first 10 Fibonacci numbers."], | |
| ["2", "Could you explain how the code works?"], | |
| ["3", "What is recursion?"], | |
| ] | |
| } | |
| ], | |
| "Identity": [ | |
| { | |
| "title": "Conversation with the planet Pluto", | |
| "examples": [ | |
| ["1", "Conversation with the planet Pluto", "I'am so curious about you"], | |
| ["2", "Conversation with the planet Pluto", "Tell me what I would see if I visited"], | |
| ["3", "Conversation with the planet Pluto", "It sounds beautiful"], | |
| ["4", "Conversation with the planet Pluto", "I'll keep that in mind. Hey I was wondering have you ever had any visitor?"], | |
| ["5", "Conversation with the planet Pluto", "That must have been exciting"], | |
| ["6", "Conversation with the planet Pluto", "That's so great. What else do you wish people knew about you?"], | |
| ["7", "Conversation with the planet Pluto", "Thanks for talking with me"], | |
| ] | |
| }, | |
| { | |
| "title": "Conversation with a paper airplane", | |
| "examples": [ | |
| ["1", "Conversation with a paper airplane", "What's it like being thrown through the air"], | |
| ["2", "Conversation with a paper airplane", "What's the worst place you've ever landed"], | |
| ["3", "Conversation with a paper airplane", "Have you ever stucked?"], | |
| ["4", "Conversation with a paper airplane", "What's the secret to a really good paper airplane?"], | |
| ["5", "Conversation with a paper airplane", "What's the farthest you've ever flown?"], | |
| ["6", "Conversation with a paper airplane", "Good to talk to you!"] | |
| ] | |
| } | |
| ] | |
| } | |
| SPECIAL_STRS = { | |
| "continue": "continue.", | |
| "summarize": "what have we discussed so far? describe in the user's view and include important entities. also be brief as much as possible." | |
| } |